Introduction

Concerns over the potential impact of localised depletion of krill through concentrated fishing effort on krill-dependent predators has been a topic of debate within SC-CAMLR and its Working Groups for many years (REF). Recently, two studies have been presented that suggest that local harvesting rates can impact predator performance to the same degree as poor environmental conditions (Watters et al., 2020) and when poor climactic conditions are coupled to locally high harvest rates the synergistic impacts on predators are evident (Krüger et al., 2021).

While both studies attempt to tackle the same overall problem, they do so using very different methodologies. Watters et al. (2020) exploit a considerable dataset; a substantial multi-species time series of penguin performance indices (including those collected under CEMP) collected over three decades at two sites (Cape Shireff on Livingstone Island and Copacobana on King George Island, South Shetland Islands; Figure 1) and over a decade of summer acoustic surveys that cover the at-sea distributions of Chinstrap, gentoo and Adélie penguins. Drawing in monthly krill catch statistics from the C1 Catch and Effort dataset and climactic data (Oceanic Niño Index; ONI), the authors use a hierarchical analysis of variance approach to estimate the variance in performance indices as a function of Local Krill Biomass (LKB), Local Harvesting Rates (LHR; the ratio of krill catch to LKB) and ONI. In contrast, Krüger et al. (2021) utilise a broader range of penguin colonies across the same three species throughout the Antarctic Peninsula area, in combination with their respective abundance survey estimates from an open-source database (www.penguinmap.org). The authors calculate population trends for appropriate sites, and using the CCAMLR C1 Catch and Effort data to extract annual catch values within a 30km radius of each colony. Finally, Krüger et al. (2021) use the trajectory of the trend (positive; increase versus negative; decrease) as a response in a binomial generalised linear mixed model using the summed annual catch, a lagged trend of the Southern Annular Mode and the SOMETHING ON ONI ???? to determine the relative contributions of each predictor and their interactive effects on population abundance trends. Both studies draw similar conclusions; that local harvesting levels of krill impact predators, and the degree of impact can either be similar to that of poor environmental conditions or have a synergistic impact when high local harvesting coincides with poor conditions.

These conclusions have been propogated into Commission documentation supporting the reformulation of the D1MPA proposal (CCAMLR-39/BG/02) as well as into Commission discussions (CCAMLR-39, Para 5.48 & Para 5.51). However, while the two studies have moved from Working Papers of EMM into the realms of the peer-reviewed literature, we have some areas of concern about the structuring of these studies that we feel warrant raising. Our preliminary review raises concerns unique to each study but also common across both, and we structure our paper accordingly. Firstly, we review Watters et al. (2020) and Krüger et al. (2021) through the lens of some of the ecological assumptions made versus the available evidence pertaining to them. Within the constraints of the data and analytical methods that are available from the studies, we also quantify how rationalising these assumptions to the evidence available impacts the conclusions drawn. We then highlight some overarching concerns applicable to both papers.

Watters et al. (2020) / WG-EMM 2019/11

A key motivation for the paper appears to be to highlight the mismatch between the areal scales of fisheries management and ecological the interactions between fishing extractions and dependent predators. To do this, the authors create two strata aligned with groups of SSMU (gSSMU); gSSMU #1 including those SSMU inside the Bransfield Strait (APBSE and APBSW and gSSMU #2 incorporating SSMU north of the South Shetlands, including Elephant Island (APDPE, APDPW and APEI) represented in Figure 1. These gSSMU cover \(15,500nm^2\) and \(20,600nm^2\), respectively, and are used to characterise both krill biomass and harvesting rates that are “local” to the penguin colonies for which performance data are used. The reasoning behind scaling to gSSMU are linked to the foraging behaviour of the penguins for which performance data area available i.e. breeding, adult pygoscelids.

The authors cite Hinke et al. (2017) as the evidence supporting usage of the two gSSMU as appropriate strata. Pygoscelid penguins exhibit staggered breeding, with Adélies commencing first, followed by chinstraps then gentoos (Black, 2016). Adélie penguins are the first to fledge their chicks and thus cease to be centrally foraging, typically departing mid-February for their moulting grounds on the sea ice. Chinstrap penguins depart for a pre-moult foraging trip towards the end of February and return to land in order to moult, before departing again for their overwinter trip (Hinke et al. (2015); Hinke et al. (2019); Figure 2). Conversely, Gentoo penguins appear to remain in close proximity to their breeding colonies overwinter .

We use the Argos-CLS PTT telemetry data provided with this supporting study to characterise the actual at-sea habitat used, in the context of the relative stage of breeding for each species. For each species, we exclude locations with a “Z” error class and calculate the 99% Minimum Convex Polygon (home range) and associated area in \(nm^2\). For chinstrap penguins at Cape Shireff, this equates to a home range area of ~\(4,782nm^2\), or only 23% of the gSSMU to which their performance metrics are indexed against (Watters et al., 2020). For the same species at Copacobana the 99% MCP home range is 2,905\(nm^2\), or ~19% of gSSMU 1 in the Bransfield Strait. Similarly for Adélie penguins, the breeding foraging range occupied 1,139\(nm^2\) or only ~7% of the area of gSSMU #1. After breeding, available overwinter PTT telemetry and light geolocating data on chinstrap and adelie penguins suggests a wide dispersal westwards into the Pacific sector of the Southern Ocean, and eastwards into the Weddell Sea and Atlantic sectors, with a relatively small proportion of chinstraps from the study sites remaining within 500km of their breeding colonies (Hinke et al., 2019). Yet despite the evidence supporting widescale post-breeding migration of both Adélie and Chinstrap penguins, the model used by Watters et al. (2020) constrains both species from Copacobana to gSSMU 1 and Chinstraps from Cape Shireff to gSSMU 2 over winter (Watters et al. (2020); Supplementary Material 1 & 2, code lines 258 to 259). This has the effect of constraining the variability in performance indices from these species to LHR, LKB and ONI over winter in areas where the species has a demonstrated tendency to migrate out of. This is particularly important given that the fishery can now be characterised with a late autumn/early winter start (Figure 4).

Our preliminary review thus raises two areas of concern. Firstly, that the scales at which “local” predictors are summarised are in some cases five times greater than the habitat exploited by penguins. Secondly, that the known overwinter migratory behaviour of Adélie and Chinstrap penguins are poorly reflected in the model formulation. To demonstrate the impact that these ecological assumptions have on the model output, we rerun the model of Watters et al. (2020) with modified code. To avoid an overly burdensome paper, we shortly summarise those code changes here, and if requested during the meeting we are happy to include the rmarkdown version of this paper with the modified code in place.

However, we also note an additional coding error that may influence how the original (i.e. unmodified) results are interpreted. In summarising the model outputs into boxplots, the code relating to developing Figure 2 (Supplementary Material 1, lines 661-665) seemingly classifies the “Worst Case” (\({-0.5}\) \(^{\circ}\)C < ONI < 0.5 \(^{\circ}\)C; LKB > 1 Mt; and LHR \(\geqslant\) 0.1) using column 36 from the output dataframe, which actually reflects “ONI > 0.5 \(^{\circ}\)C; LKB > 1 Mt; and LHR \(\geqslant\) 0.1”; that is, ONI is in its “warm” phase as opposed to “neutral”. We thus relabel the output boxplot axis labelling to reflect the conditions representing “worst case” accordingly.
modifications

  1. We scale the gSSMU LKB to the SSMU that the summer tracking data indicate penguins occupied. For example, we scale LKB for Cape Shireff chinstrap penguins solely to ADPDW by multiplying the gSSMU LKB by the areal ratio of ADPDW/gSSMU #2. We then select the corresponding SSMU catch values provided in Watters et al. (2020) to estimate SSMU-scale LHR.
  2. We remove Adélie and Chinstrap penguins from the model formulation over winter; that is, we attribute each species as “NA” during winter, to account for dsipersal after breeding.
    results

Krüger et al. (2021) / WG-EMM 2019/10

NEEDS WORKING ON - A LOT :) BULLET POINTS FROM THE GOOGLE DOC BELOW:

  1. Contention that population trends (rate of growth/decline) are related to synergistic impacts of climate change and localized fishing pressure
  2. Autumn/winter SAM is an appropriate proxy for krill abundance (i.e no consideration of interactive effects between SAM and ONI)
  3. The validity of linear interpolation across multiple years (>1) has not been analysed.
  4. 30km buffer around colonies to extract fishing effort is appropriate over winter
  5. Using population rates calculated between survey efforts sometimes several years apart are appropriate
  6. Assumption that calculated population rates directly reflect mortalities
  7. No consideration of lagged recruitment (fledging to reproductive age) nor ability to detect lagged recruitment with irregular surveying effort / lack of banding

Discussion

Our preliminary review of the evidence supporting localised effects of fishing coupled with broad-scale climactic phenomena having an impact on the vital statistics of pygoscelid penguins (performance and demographic trends) are based on assumptions that potentially do not reflect current knowledge of penguin breeding phenology and movement.

DESCRIPTION OF x6 SCENARIOS RUN WITH STIG

Of greatest concern, however, is that the interpretation of model outputs from both approaches (either from the original studies or the modified parameters we describe) are under boundary conditions that we feel are not appropriate. Both approaches consider only the fishery and broad-scale climate phenomena as the only two causes of krill abundance variability at geographic scales relevant to penguins. Neither study considers, for example, the impact of rebounding baleen whale populations or migratory male Antarctic fur seals beyond brief mentioning. Both taxa have increased in abundance throughout the life of the krill fishery, and there are sufficient telemetry and distance sampling studies in the scientific literature to demonstrate the degree and significance of spatiotemporal overlap with breeding penguin populations (see Santora and Veit (2013), Lowther et al. (2020), and Oosthuizen et al., Johannessen et al. and Lowther et al. submitted to this meeting, and Figure 3 as examples). Importantly, the distribution of these and numerous other unconsidered competitors is not uniform in either space or time and their impact on local availability of krill is likely to be considerable.

Similarly, the utilisation of broad-scale climatological phenomena to characterise impacts at scales that predators are dependent upon is problematic. The Amundsen Sea Low (ASL) is the dominant climate feature for the western Antarctic Peninsula. The El Niño Southern Oscillation (ENSO) modulates the the ASL, with El Niño (La Niña) shallowing (deepening) its pressure, causing more northwesterly (southeasterly) winds and upwelling (restricted influx) of Circumpolar Deep Water onto the shelf. The Southern Annular Mode also influences the pressure of the ASL, with the current trend of negative SAM constructively (destructively) interfering with ASL when in phase with El Niño (La Niña) events (e.g. Clem et al. (2016)). The result is a set of above-surface climate conditions that drive changes in water mass intrusion that are dependent on interactions between two climate processes. However the bathymetry of the Antarctic Peninsula is complex (particularly at scales that are important to centrally-foraging predators such as penguins) and the structuring of krill aggregations in time and space in the WAP have been linked to mesoscale circulation processes (Santora et al., 2012), which are unlikely to be uniformly affected by macroscale processes.

Our work into the future will progress along three lines, and we welcome any and all offers of collaboration into this work. Firstly, we will progress this debate into the scientific literature in order to ensure a balanced discussion occurs in that forum. Secondly, we will be examining in further detail some of the additional predictors used and their efficacy, the modelling frameworks into which they are brought, and how their incorporation influences the interpretation of the responses. Finally, we shall also be exploring alternative modelling approaches that reflect more of the physical and biological complexity of the system in question. In all cases, our goal is to ensure that the best available objective scientific evidence is presented to our environmental managers and, where appropriate, flag that disagreement exists. Our paper should be viewed in this light to generate constructive dialogue that addresses our common concern of the potential for localised fishing to impact dependent aspects of the ecosystem.

Citations

Penguin foraging behaviour derived from available ARGOS-CLS PTT data. A) Chinstrap penguins truncated at $10^{th}$ March in line with known phenology (elongated grey track represents a single animal) B) Adélie penguins truncated to the end of January and C) Gentoo penguins until ~August, in line with known overwinter foraging behaviour. The SSMU are combined and coloured according to gSSMU (red; gSSMU 2, purple; gSSMU 1) with Chinstrap and Adélie penguin 99\% MCP home ranges occupying between 7-19\% of the gSSMU to which they were assigned.

Penguin foraging behaviour derived from available ARGOS-CLS PTT data. A) Chinstrap penguins truncated at \(10^{th}\) March in line with known phenology (elongated grey track represents a single animal) B) Adélie penguins truncated to the end of January and C) Gentoo penguins until ~August, in line with known overwinter foraging behaviour. The SSMU are combined and coloured according to gSSMU (red; gSSMU 2, purple; gSSMU 1) with Chinstrap and Adélie penguin 99% MCP home ranges occupying between 7-19% of the gSSMU to which they were assigned.


A) Distribution of overwinter movement for Chinstrap penguins, relative to the gSSMU's to which they were attributed, created from telemetry data available in @Hinke2019. B)Adélie and Chinstrap penguin movement recorded by light geolocators, highlighting the large longitudinal range both species disperse through at the end of breeding (taken from @Hinke2015). In the original model formulation by Watters et al. 2020, the performance indices for both species are matched to gSSMU-scale estimates of LKB and LHR, but macroscale levels of ONI variability.

  1. Distribution of overwinter movement for Chinstrap penguins, relative to the gSSMU’s to which they were attributed, created from telemetry data available in Hinke et al. (2019). B)Adélie and Chinstrap penguin movement recorded by light geolocators, highlighting the large longitudinal range both species disperse through at the end of breeding (taken from Hinke et al. (2015)). In the original model formulation by Watters et al. 2020, the performance indices for both species are matched to gSSMU-scale estimates of LKB and LHR, but macroscale levels of ONI variability.


The summer distribution of foraging effort by A) adult female Antarctic fur seals (adapted from telemetry data available in Hinke et al. 2017), B) migratory adult male Antarctic fur seals (adapted from Lowther et al. 2020) C) humpback whales throughout December (adapted from Johannessen et al., this meeting) and D) nonbreeding adult Adélie penguins during the breeding season (adapted from data in Oosthuizen et al., this meeting). Potential effects of competitive overlap between pygoscelid penguins and other krill dependent predators, particularly those who have increased their abundance dramatically over the preceding 40 years, should be considered when attempting to interpret variance in penguin vital rates.

The summer distribution of foraging effort by A) adult female Antarctic fur seals (adapted from telemetry data available in Hinke et al. 2017), B) migratory adult male Antarctic fur seals (adapted from Lowther et al. 2020) C) humpback whales throughout December (adapted from Johannessen et al., this meeting) and D) nonbreeding adult Adélie penguins during the breeding season (adapted from data in Oosthuizen et al., this meeting). Potential effects of competitive overlap between pygoscelid penguins and other krill dependent predators, particularly those who have increased their abundance dramatically over the preceding 40 years, should be considered when attempting to interpret variance in penguin vital rates.


Daily accumulated catch (and fitted LOESS smooth curves) in Subarea 48.1 between 20$^{th}$ February and March 31$^{st}$ between A) 1990-2010 and B) 2010 to 2020. Catch in the Subarea was relatively consistent during the latter stages of penguin breeding at approximately 250tonnes/d. However, since 2010 there has been a tendency for the fishery to increase its effort in the Subarea, starting around the middle of March. C) During this latter period, the fishery increased effort earlier on three occasions (2013, 2015 and 2016), starting before the beginning of March.

Daily accumulated catch (and fitted LOESS smooth curves) in Subarea 48.1 between 20\(^{th}\) February and March 31\(^{st}\) between A) 1990-2010 and B) 2010 to 2020. Catch in the Subarea was relatively consistent during the latter stages of penguin breeding at approximately 250tonnes/d. However, since 2010 there has been a tendency for the fishery to increase its effort in the Subarea, starting around the middle of March. C) During this latter period, the fishery increased effort earlier on three occasions (2013, 2015 and 2016), starting before the beginning of March.

Black, C.E., 2016. A comprehensive review of the phenology of Pygoscelis penguins. Polar Biology 39, 405–432. doi:10.1007/s00300-015-1807-8

Clem, K.R., Renwick, J.A., McGregor, J., Fogt, R.L., 2016. The relative influence of ENSO and SAM on antarctic Peninsula climate. Journal of Geophysical Research 121, 9324–9341. doi:10.1002/2016JD025305

Hinke, J.T., Cossio, A.M., Goebel, M.E., Reiss, C.S., Trivelpiece, W.Z., Watters, G.M., 2017. Identifying Risk: Concurrent overlap of the antarctic krill fishery with krill-dependent predators in the scotia sea. PLoS ONE 12, e0170132. doi:10.1371/journal.pone.0170132

Hinke, J.T., Polito, M.J., Goebel, M.E., Jarvis, S., Reiss, C.S., Thorrold, S.R., Trivelpiece, W.Z., Watters, G.M., 2015. Spatial and isotopic niche partitioning during winter in chinstrap and Adélie penguins from the South Shetland Islands. Ecosphere 6, 125. doi:10.1890/ES14-00287.1

Hinke, J.T., Santos, M.M., Korczak-Abshire, M., Milinevsky, G., Watters, G.M., 2019. Individual variation in migratory movements of chinstrap penguins leads to widespread occupancy of ice-free winter habitats over the continental shelf and deep ocean basins of the Southern Ocean. PLoS ONE 14. doi:10.1371/journal.pone.0226207

Krüger, L., Huerta, M.F., Santa Cruz, F., Cárdenas, C.A., 2021. Antarctic krill fishery effects over penguin populations under adverse climate conditions: Implications for the management of fishing practices. Ambio 50, 560–571. doi:10.1007/s13280-020-01386-w

Lowther, A.D., Staniland, I., Lydersen, C., Kovacs, K.M., 2020. Male Antarctic fur seals: Neglected food competitors of bioindicator species in the context of an increasing Antarctic krill fishery. Scientific Reports 10. doi:10.1038/s41598-020-75148-9

Santora, J.A., Sydeman, W.J., Schroeder, I.D., Reiss, C.S., Wells, B.K., Field, J.C., Cossio, A.M., Loeb, V.J., 2012. Krill space: A comparative assessment of mesoscale structuring in polar and temperate marine ecosystems. ICES Journal of Marine Science 69, 1317–1327.

Santora, J.A., Veit, R.R., 2013. Spatio-temporal persistence of top predator hotspots near the Antarctic Peninsula. Marine Ecology Progress Series 487, 287–304. doi:10.3354/meps10350

Watters, G.M., Hinke, J.T., Reiss, C.S., 2020. Long-term observations from Antarctica demonstrate that mismatched scales of fisheries management and predator-prey interaction lead to erroneous conclusions about precaution. Scientific Reports 10. doi:10.1038/s41598-020-59223-9